Conference proceeding
Accelerated Parallel MRI Using Memory Efficient and Robust Monotone Operator Learning (MOL)
2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp.1-4
04/18/2023
DOI: 10.1109/ISBI53787.2023.10230471
PMCID: PMC11087020
PMID: 38738185
Abstract
Model-based deep learning methods that combine imaging physics with learned regularization priors have been emerging as powerful tools for parallel MRI acceleration. The main focus of this paper is to determine the utility of the monotone operator learning (MOL) framework in the parallel MRI setting. The MOL algorithm alternates between a gradient descent step using a monotone convolutional neural network (CNN) and a conjugate gradient algorithm to encourage data consistency. The benefits of this approach include similar guarantees as compressive sensing algorithms including uniqueness, convergence, and stability, while being significantly more memory efficient than unrolled methods. We validate the proposed scheme by comparing it with different unrolled algorithms in the context of accelerated parallel MRI for static and dynamic settings.
Details
- Title: Subtitle
- Accelerated Parallel MRI Using Memory Efficient and Robust Monotone Operator Learning (MOL)
- Creators
- Aniket Pramanik - University of IowaMathews Jacob - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp.1-4
- DOI
- 10.1109/ISBI53787.2023.10230471
- PMID
- 38738185
- PMCID
- PMC11087020
- NLM abbreviation
- Proc IEEE Int Symp Biomed Imaging
- eISSN
- 1945-8452
- Publisher
- IEEE
- Language
- English
- Date published
- 04/18/2023
- Academic Unit
- Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984461957002771
Metrics
19 Record Views